Background: There has been little systematic analysis of the impact of socioeconomic disparities on managed care utilization. Explaining utilization variations at both patient and physician profiling level has largely been limited to age/sex and utilization-derived diagnostic information. This deficit exists despite: 1) a socioeconomic gradient in health and health care use; 2) that poorer persons have worse outcomes in managed care compared to the fee-for-service environment; and 3) managed care's responsibility to optimize care for a defined population. Aims: 1) to explore the relationship between census-based socioeconomic indicators (derived using geocoded patient addresses) and utilization at the individual patient level; and 2) to explore the relationship between the census-based socioeconomic indicators and utilization at the physician level. Subjects: Approximately 500,000 persons (50 percent of the local population) enrolled in a managed care organization in the Rochester, NY, metropolitan area and their primary care physicians. Methods: Patient addresses will be geocoded and the geocoded information will be linked to block group level socioeconomic indicators in the 1990 Census. Besides analyzing the relationship between the socioeconomic indicators and utilization (for disease-related visits, avoidable and other hospitalizations, preventive care, chronic disease management, referrals, non-use, and expenditures) at the individual patient level, the value of these indicators for physician profiling will also be explored using the claims data. Significance: This will be one of few studies in the U.S. to examine systematically the relationship between health care utilization and socioeconomic factors in a managed care environment. The proposal will develop a methodology that easily allows the relationship between socioeconomic factors and health care utilization to be monitored. Unlike adjustment using proprietary systems based on prior utilization, the proposed approach is an easily applied, accessible methodology that is resistant to physician gaming, allows adjustment to local needs, and meaningfully adjusts for population differences in prevention uptake, chronic disease management compliance, and health care non-use. Beyond enabling the monitoring of socially-based health care inequalities, the approach may enable more appropriate adjustment for physician profiling, and contribute to the development of more effective, targeted interventions to improve quality.